Boolean indexing in Pandas - Mini Project: Build & Apply

Boolean Indexing with pandas

📖 Scenario: You work in a small store and have a list of products with their prices. You want to find which products cost more than $20 to decide what to promote.

🎯 Goal: Use boolean indexing in pandas to filter products that cost more than $20.

📋 What You'll Learn

Create a pandas DataFrame with product names and prices

Create a boolean condition to select products with price greater than 20

Use boolean indexing to filter the DataFrame

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores often need to filter products by price to create promotions or discounts.

💼 Career

Data analysts use boolean indexing in pandas to quickly filter and analyze data based on conditions.

1

Create the products DataFrame

Import pandas as pd and create a DataFrame called products with these exact entries: product names 'Pen', 'Notebook', 'Eraser', 'Backpack', and prices 5, 25, 3, 45 respectively.

Pandas

import pandas as pd
# Create the DataFrame called products with the given product names and prices
# Your code here

Hint

Use pd.DataFrame with a dictionary containing keys 'product' and 'price'.

2

Create a boolean condition for prices over 20

Create a variable called expensive that holds a boolean Series selecting rows from products where the price is greater than 20.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
# Create the boolean Series expensive for prices > 20
# Your code here

Hint

Use products['price'] > 20 to create the boolean Series.

3

Filter products using boolean indexing

Create a new DataFrame called expensive_products by using boolean indexing on products with the expensive variable.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
expensive = products['price'] > 20
# Use boolean indexing to create expensive_products DataFrame
# Your code here

Hint

Use products[expensive] to filter the rows.

4

Print the filtered DataFrame

Print the expensive_products DataFrame to show only products with price greater than 20.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
expensive = products['price'] > 20
expensive_products = products[expensive]
# Print the expensive_products DataFrame
# Your code here

Hint

Use print(expensive_products) to display the filtered DataFrame.

Practice

1. What does boolean indexing in pandas do?

easy

A. Changes the data type of columns

B. Sorts the DataFrame by column values

C. Selects rows based on True/False conditions

D. Merges two DataFrames together

Solution

1. Step 1: Understand boolean indexing

   Boolean indexing uses True/False values to filter rows in a DataFrame.

2. Step 2: Identify its purpose

   It selects only rows where the condition is True, filtering data easily.

3. Final Answer:

   Selects rows based on True/False conditions -> Option C

4. Quick Check:

   Boolean indexing = row filtering [OK]

Hint: Boolean indexing filters rows with True conditions [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Mixing it up with DataFrame merging

2. Which of the following is the correct syntax to select rows where column 'age' is greater than 30 in a DataFrame df?

easy

A. df['age' > 30]

B. df[df['age'] > 30]

C. df[df.age = 30]

D. df[df['age'] => 30]

Solution

1. Step 1: Check correct boolean indexing syntax

   Use df[condition], where condition is df['age'] > 30.

2. Step 2: Identify syntax errors in other options

   df['age' > 30] misplaces brackets, C uses assignment (=) instead of comparison, D uses invalid operator =>.

3. Final Answer:

   df[df['age'] > 30] -> Option B

4. Quick Check:

   Correct syntax uses df[condition] with comparison [OK]

Hint: Use df[condition] with correct comparison operators [OK]

Common Mistakes:

• Using = instead of > for comparison
• Wrong bracket placement
• Using invalid operators like =>

3. Given the DataFrame df below:

   name  age  score
0  Anna   25     88
1  Bob    32     75
2  Cara   29     95
3  Dave   35     60
4  Ella   28     82

What will df[(df['age'] > 28) & (df['score'] > 70)] return?

medium

A. Rows with age > 28 and score > 70: Anna and Ella

B. Rows with age > 28 and score > 70: Bob and Dave

C. Rows with age > 28 and score > 70: Dave only

D. Rows with age > 28 and score > 70: Bob and Cara

Solution

1. Step 1: Identify rows where age > 28

   Rows: Bob (32), Cara (29), Dave (35) satisfy age > 28.

2. Step 2: From those, select rows with score > 70

   Bob (75), Cara (95), Dave (60). Dave fails score > 70, so only Bob and Cara remain.

3. Final Answer:

   Rows with age > 28 and score > 70: Bob and Cara -> Option D

4. Quick Check:

   Filter by both conditions = Bob and Cara [OK]

Hint: Check each condition separately, then combine results [OK]

Common Mistakes:

• Including Dave who has score 60
• Mixing up & and | operators
• Ignoring parentheses around conditions

4. What is wrong with this code snippet for filtering rows where score is above 80?

df[df['score'] > 80 & df['age'] < 30]

medium

A. Missing parentheses around conditions causing operator precedence error

B. Using & instead of | for combining conditions

C. Incorrect column name 'score'

D. DataFrame df is not defined

Solution

1. Step 1: Understand operator precedence in pandas boolean indexing

   Bitwise operators (&, |) require parentheses around each condition to avoid errors.

2. Step 2: Identify missing parentheses

   Code lacks parentheses around df['score'] > 80 and df['age'] < 30, causing wrong evaluation.

3. Final Answer:

   Missing parentheses around conditions causing operator precedence error -> Option A

4. Quick Check:

   Use parentheses with & and | [OK]

Hint: Always wrap each condition in parentheses when using & or | [OK]

Common Mistakes:

• Forgetting parentheses around each condition
• Confusing & with logical and (and)
• Assuming no error without parentheses

5. You have a DataFrame df with columns 'city', 'temperature', and 'humidity'. You want to select rows where city is either 'Paris' or 'London' and temperature is above 20. Which code correctly applies boolean indexing?

hard

A. df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)]

B. df[df['city'] == 'Paris' | df['city'] == 'London' & (df['temperature'] > 20)]

C. df[df['city'] == ('Paris' or 'London') & df['temperature'] > 20]

D. df[(df['city'] == 'Paris') | (df['city'] == 'London') & (df['temperature'] > 20)]

Solution

1. Step 1: Understand condition grouping with parentheses

   Conditions combined with | must be wrapped in parentheses to group city checks correctly.

2. Step 2: Check each option for correct syntax and logic

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] correctly groups city conditions and combines with temperature condition using &.

3. Final Answer:

   df[((df['city'] == 'Paris') | (df['city'] == 'London')) & (df['temperature'] > 20)] -> Option A

4. Quick Check:

   Group OR conditions with parentheses before AND [OK]

Hint: Group OR conditions in parentheses before AND conditions [OK]

Common Mistakes:

• Missing parentheses causing wrong condition order
• Using Python 'or' instead of |
• Trying to compare city to ('Paris' or 'London') directly